重庆市主城区气象条件对空气污染影响分析及数值模拟研究
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摘要
重庆市是我国地形复杂、幅员面积最大的城市,也是我国最早出现雾霾型空气污染、且较为严重的城市之一,这不仅给当地居民的健康带来不同程度的危害,也影响了重庆市的城市投资环境和竞争力。基于此,本文首先分析了重庆市主城区主要大气污染物(PM10、SO2、NO2)的空间分布及年际、年和日变化特征；其次,采用K-means聚类方法对重庆污染天气大气环流进行分类,探析了污染天气过程特征及本地相关气象要素的变化与污染浓度的相关性,仔细研究了不同性质降水对大气污染物的清除效率；最后,采用观测资料分析和WRF-Chem数值模拟,重点研究了重庆主城区在晴天、阴天和雾天三种主要天气背景下,局地边界层气象特征及其变化对污染物浓度的影响,初步揭示了不利气象条件造成空气污染的影响机制。主要研究结果如下：
(1)重庆主城区空气污染空间分布特征是：PM1o高污染区为工商业发达的主城核心六区,SO2高污染区主要分布在工业区,NO2高污染区主要出现在商业最繁华、车流量大的区域。
(2)重庆主城区主要污染物(PM10、SO2、NO2)浓度的时间变化特征是：其年际变化总体呈下降趋势,空气质量呈逐年好转态势。年变化中主要污染期为秋末、冬季到初春(11月～次年3月),尤以12月最为严重,其次是11月,夏季污染最轻。PM1o逐时平均浓度呈现典型的“双峰双谷”日变化特征,具有12小时左右的变化周期,冬半年在12:00和22:00前后出现峰值,在7:00和18:00前后出现谷值,夏半年在11：00和23:00前后出现峰值,在7:00和17:00前后出现谷值；SO2平均浓度呈现“单峰”日变化特征,冬半年在12:00-13:00达到峰值,夏半年一般在10:00～11：00达到峰值；NO2平均浓度呈现不太典型的“双峰双谷”日变化特征,NO2主峰值出现在20:00前后,10:00-12:00前后的峰值不太明显。
(3)采用K-means聚类方法对造成重庆主城区空气污染的500hPa大气环流形势进行分型,大致可分为一槽一脊型、纬向环流型、两槽一脊型、西高东低型和低槽东移型5种类型。在连续性污染天气过程中,地面目平均温度变化总体呈逐日上升趋势,日平均气压总体呈逐日下降趋势,日平均相对湿度变化主要表现为污染前期湿度先降低,后期逐渐增大,风速的变化趋势不明显,基本维持在1m/s～1.5m/s之间。
(4)分析表明,重庆主城区边界层气象条件对污染物浓度有最直接的影响。在雾天、晴天和阴天三种典型代表性天气中,从夜间到清晨,出现逆温频率高且空风速较小,但由于夜间污染源排放减弱,污染物浓度呈现自然下降态势；其中雾天由于相对湿度大,水汽的吸附(转化)作用,污染物浓度下降速度比晴天和阴天快。8:00以后由于人群活动及污染排放量的增加,污染物浓度在上述三种代表性天气背景下均呈快速上升态势；从8:00到13:00,阴天升温不及雾天和晴天快且高低空风速也比较小,污染物浓度上升速度也比雾天和晴天快；13:00以后,三种代表性天气背景下重庆主城区边界层逆温均基本或完全消失,大气垂直扩散能力明显增强,污染物浓度开始明显下降；其中,晴天增温快,午后温度高,风速大,PM10浓度下降速度也快；雾天温度增速不及晴天快、且地面风速变化不大,因此PM10浓度降低速度低于晴天；阴天由于午后增温速度明显比晴天和雾天慢,增温幅度也小,风速也小,因而PM10浓度降低速度慢,相对污染浓度高。
(5)通过逆温与昼夜温差相关性分析结果表明,与其他北方城市(如兰州市等)不同,重庆主城区由于冬半年昼夜温差小导致逆温强度弱、厚度薄,从而造成其冬半年轻度污染天气相对较多,很少出现中度及以上污染天气,这一点与北方城市冬季易出现重度污染的状况存在明显的差异。对重庆市大气扩散能力的分析结果也表明,湍流动能越强,污染物的垂直扩散能力越强,污染物浓度越低；但总体而言,重庆主城区污染物在大气中水平方向由平均风速完成的平流输送还是明显强于由湍流完成的垂直方向的输送与扩散。
(6)数值模拟结果显示,WRF-Chem模式对重庆主城区大气边界层气象场有较好的模拟效果,其中MYJ方案对边界层温度场和风场模拟效果优于YSU方案。WRF-Chem模式基本能够模拟出重庆主城区PM10、SO2和N02浓度的日变化特征,但浓度值偏小,对污染物浓度模拟效果同样也是MYJ方案优于YSU方案。详细模拟结果表明,边界层高度(PBLH)能显著影响污染物浓度,通过计算8:00-17：OOPBLH与PM10的相关系数显示,PBLH与1～3小时后的PM10浓度负相关较好,表明白天PBLH明显增大1～3小时后PM10浓度才明显降低。不同天气背景下也略有变化,其中在雾天和阴天,PBLH;斗高后2-3小时PM10浓度才有明显下降,而在晴天PBLH升高后1～2小时PM10浓度就会明显下降,表明大气边界层气象条件变化速度越快,污染物稀释扩散响应时间也越短。上述PM10浓度随PBLH变化而变化的事实,能够较好地解释PM10日变化特征的成因。通过数值模拟还发现,边界层风场对污染物浓度变化也有明显的作用,在夜间,重庆主城区主要受下沉气流影响,对污染物的向上垂直扩散作用非常弱；在白天上午,由于建筑施工、机动车尾气等污染排放量的增加,当受逆温或夜间下沉气流持续控制时污染物的向上垂直扩散作用很弱,近地面污染物浓度会快速增加；相反,当日出后边界层专为上升气流时污染物的向上垂直扩散能力会增强,污染物浓度的增加速度会减慢,尤其在高空风速大且为强上升气流时,强烈的抽吸作用使得大气垂直扩散能力得到显著增强,能有效地抑制污染物浓度的增长态势。通常到午后,太阳辐射强烈,大气边界层内上升气流达到最强,PBLH达到最大,大气边界层垂直扩散能力最强,因而污染物浓度达到一日中的谷值点。在污染天气向非污染天气转换过程中,大气边界层的风速会明显增强且为上升气流,边界层高度会明显增高,湍流动能也明显增强,受大气的水平输送能力和垂直扩散能力都增强的共同作用,重庆主城区大气污染物浓度会明显下降,空气质量转好,由此较为细致地揭示了该区边界层气象条件的变化对空气污染影响的过程与机理。
(7)研究表明,降水对重庆主城区空气污染物也有明显的清除作用,不同强度等级的日降水对PM10、SO2、N02清除效率和对API值降低率均呈现指数变化；连续性降水对污染物清除最有效的是前两天降水,且第二天降水对污染物的清除效率比第一天要高。逐时降水对PM10和S02清除效果好于N02;夜间降水对污染物清除效率比白天降水高;此外降雨对污染浓度上升阶段清除效果好于下降阶段。
基于上述对重庆主城区空气污染时空变化特征及其与气象条件的研究,并利用降水能有效清除污染物的机理,结合该区首要污染物为PM10及其特殊的日变化特点,我们建议可通过局部的试验研究,然后在20::00～次日7:00(夜间)选择火箭、飞机增雨或高楼喷水等作业方式在重庆主城区进行人工干预措施,达到快速清除低空污染物,有效改善空气质量之目的；若选择在上午8:00～12:00可针对PM10浓度出现上升时间段内,通过飞机增雨或高楼喷水等方式进行作业,可能对抑制PM10浓度的升高会起到更好的效果。
As the biggest Chinese city in size and with complex terrain, Chongqing is one of the earliest type of fog-haze air pollution, and the most air-polluted cities in China, which has not only brought harms to health of the citizens, but also influenced the city's investment environment and competitiveness. Based on current situation, this paper firstly, studies the spatial distribution features and annual, monthly and diurnal variation of three main air pollutants (PM10, SO2, and NO2) in the urban areas of Chongqing from2002to2011. Secondly, method of K-means clustering analysis is used to classify the upper air and ground surface weather conditions which affect the diffusion of contaminant. The typical weather types that block the diffusion of pollutants summed up. The paper also analyzes the weather process characteristics of air pollution and the correlation between the local meteorological elements and the pollution concentration.The removal efficiency on atmospheric pollutants of different properties of precipitation is minutely discussed. Moreover, using the WRF-Chem model and observed data analysis, the paper emphatically studies the effects of local boundary layer meteorological features on the pollutants concentration under the conditions of sunny days, cloudy days and fogs in the urban areas of Chongqing. Mechanism of how meteorological conditions lead to serious air pollution is preliminarily uncovered. The main findings of the research are as follow:
(1) The spatial distribution characteristics of air pollution in the main urban region of Chongqing:the highest PM10pollution areas are the six industrial and commercial core areas, and the SO2pollutant is mainly distributed in the industrial area, while the NO2pollutant mainly appears in the most prosperous business areas and busy roads.
(2) The annual variation of the three main pollutants (PM10, SO2and NO2) concentration in the urban areas of Chongqing is declining on the whole, and the air quality is improved year by year. The highest pollution period is late autumn, winter and early spring (from November to March), especially in December and followed by November. The hourly average concentration of PM10presents the characteristics of "double peak and double valley" diurnal variation with a period of12hours. The peak appears at around12:00and22:00in winter half, while the lowest value appears at around7:00and18:00. But, the peak appears at around11:00and23:00in summer half, while the lowest value appears at around7:00and17:00.The concentration of SO2presents the characteristics of "single peak" diurnal variation. The peak appears at about12:00to13:00in winter half and10:00to11:00in summer half. The average of NO2concentration presents the characteristics of insignificant "double peak and double valley" diurnal variation. The main peak appears clear at approximately20:00and weak at about10:00to12:00.
(3) The500hPa weather situations are classified by the method of K-means clustering analysis in Chongqing. The result shows that the high altitude weather situations are mainly the one trough and one ridge, zonal circulation, two troughs and one ridge, west high and east low and trough moving eastward. In the period of air pollution, the overall trend of the daily average surface temperature is increasing, the daily average pressure is decreasing, the daily average relative humidity changes mainly for pre period of pollution is decreasing first, then gradually increasing, and the wind speed remains from1m/s to1.5m/s.
(4) Analysis showed that the main city of Chongqing meteorological boundary conditions have the most direct impact on the concentration of pollutants. From night to morning, because three typical representative weather conditions of foggy, sunlit and cloudy are in the thermal inversion layer and wind speed is small in high altitude and near the surface, the upward spread of PM10is weak. The pollutant concentration is significantly weakened with the declination of pollution emissions. Due to the high relative humidity and the adsorption of water vapor, the rate of pollutants concentration declines faster that in the fog than sunny and cloudy. After8:00, as the increase of pollution emissions by human activity, the pollutant's concentration rises fast. However, during the8:00to13:00, the temperature rises more slowly in cloudy days than in sunny days and fogs, and the wind speed is too small in cloudy days directly leading to that the rate of pollutants concentration increasing is faster in cloudy days than that in sunny days and fogs. After13:00, boundary layer inversion is disappeared under the background of the three weather types. Therefore, the atmospheric diffusion is notably enhanced and pollutants concentrations begin to decline obviously. There are higher temperature and faster warming in sunny days, and the wind speed is large in high latitude and near the surface, so the concentrations of PM10decrease fast. The rate of warming is slower in foggy days than sunny days, so the rate of PM10concentration declining is smaller than in sunny days, which is similar in cloudy days.
(5) Through comparative analysis of inversion and diurnal temperature showed that the reason of weak inversion and thin inversion is small temperature difference between day and night in winter months in main city of Chongqing, unlike other northern cities (such as Lanzhou, etc.). Just the characteristic of weak inversion and thin inversion is caused slightly polluted weather more than moderately polluted weather in Chongqing city. This is obvious differences between in Chongqing city and in northern city. Through analysis of the changes of turbulent kinetic energy and correlation analysis of turbulent kinetic energy and pollutant concentration in different weather that the turbulent kinetic energy is stronger, vertical diffusion of pollutants is stronger, and the concentration of pollutant is lower.Diffusion capacity of pollutants is transported by the mean wind speed in horizontal advection significantly stronger than it is transported by the turbulent in vertical direction.
(6) WRF-Chem model has good simulated effects on atmospheric boundary layer in urban areas of Chongqing. Compared the two boundary layer programs, surface temperature and boundary layer wind field simulated by MYJ is better than that of YSU. The WRF-Chem model can simulate the diurnal variation of PM10, SO2and NO2concentrations in Chongqing main city, but the concentrations were significantly smaller. The concentration of pollutants simulated by MYJ is also better than that of YSU. The results of numerical simulation show that the height of boundary layer has effects significantly on pollutants concentration. As atmospheric boundary layer is stable and PBLH is relatively low at night, the vertical diffusivity of atmosphere is weak. As a result of less emission after nightfall, PM10will decrease gradually, and the pre-dawn reached its lowest point.As the increasing of solar radiation by day, PBLH increases rapidly and the vertical diffusivity space of pollutants enlarges evidently. Correlation coefficient between PBLH and PM10at8:00-17:00is examined. The result shows that PBLH is negatively related with PM101or3hours later. So, for PBLH's effect on PM10, there is a lag effect of1or3hours. The PBLH trends vary in different weather conditions, which results in obvious difference in the effects on pollutants concentration. In foggy and cloudy days, PM10decreases evidently2-3hours after increasing of PBLH. However, PM10decreases evidently1-2hours after increasing of PBLH in sunny days. The reason of PM10concentration change delay1-3hours of PBLH change is that PM10(or other contaminants) is gradually changed under the action of the atmospheric dispersion. It is a slow gradual process. SO the meteorological condition change of atmospheric boundary layer is faster, dispersion response time of pollutant is shorter.Analysis of effects of PBLH variation on PM10can better explain the characteristics of diurnal variation of PM10. Therefore, forecast of PBLH can briefly figure out the hourly variation trends of PM10, which provides technological support for pollutants forecast. The study also finds that variations of boundary layer wind field and pollutants concentration are good indicators. The urban areas of Chongqing are influenced by downdraft at night, which has weak effect on the vertical diffusivity of pollutants. Artificial emission increases in the morning. Downdraft weakens the vertical diffusivity of pollutants, which results in rapidly increasing of pollutants concentration. In the contrast, when there is updraft, the vertical diffusivity of pollutants is strong and the increasing speed of pollutants slows down. It's especially obvious when there is a high upper air wind speed and strong updraft. The strong pumping action strengthens the vertical diffusivity of atmosphere significantly to control the increasing of pollutants. In the afternoon, there is mainly strong updraft in the boundary layer as a result of strong solar radiation. The vertical diffusivity of atmospheric boundary layer is the strongest, which results in an obvious decreasing trend of the pollutants concentration, and reached the day in the valley point of the pollution concentration. When it is from pollution to non-polluted in atmospheric boundary layer, the wind speed will be significantly enhanced and is updraft, the boundary layer height will be significantly increased and the turbulent kinetic energy will be significantly enhanced. The air pollutant concentrations would be obviously reduced when horizontal diffusion and vertical diffusion capabilities are enhanced in the atmosphere.
(7)Precipitation has obvious scavenging effect on atmospheric pollutants. In addition, precipitation at different levels presents the exponential change of the scavenging effect on PM10, SO2, NO2and API reduction rate. The removal effect of continuous precipitation is best in the first two days. And the second day is better than the first day. The removal effect of hourly precipitation on PM10and SO2is better than that on NO2. And the night is better than the daytime. The removal effect of precipitation is better in the rising phase than the declining phase.
Based on the research of temporal variation of air pollution and meteorological conditions, and used of the mechanism of precipitation can effectively remove contaminants,and combined with the special primary pollutant PM10diurnal variation characteristics in the main city of Chongqing, rocket and artificial precipitation and water spray from high-rise from20:00to7:00can be used to improve air quality. And from8:00to12:00, aircraft rainfall or high-rise sprinkler can be better used to block the PM10concentration increasing.
(1)重庆主城区空气污染空间分布特征是：PM1o高污染区为工商业发达的主城核心六区,SO2高污染区主要分布在工业区,NO2高污染区主要出现在商业最繁华、车流量大的区域。
(2)重庆主城区主要污染物(PM10、SO2、NO2)浓度的时间变化特征是：其年际变化总体呈下降趋势,空气质量呈逐年好转态势。年变化中主要污染期为秋末、冬季到初春(11月～次年3月),尤以12月最为严重,其次是11月,夏季污染最轻。PM1o逐时平均浓度呈现典型的“双峰双谷”日变化特征,具有12小时左右的变化周期,冬半年在12:00和22:00前后出现峰值,在7:00和18:00前后出现谷值,夏半年在11：00和23:00前后出现峰值,在7:00和17:00前后出现谷值；SO2平均浓度呈现“单峰”日变化特征,冬半年在12:00-13:00达到峰值,夏半年一般在10:00～11：00达到峰值；NO2平均浓度呈现不太典型的“双峰双谷”日变化特征,NO2主峰值出现在20:00前后,10:00-12:00前后的峰值不太明显。
(3)采用K-means聚类方法对造成重庆主城区空气污染的500hPa大气环流形势进行分型,大致可分为一槽一脊型、纬向环流型、两槽一脊型、西高东低型和低槽东移型5种类型。在连续性污染天气过程中,地面目平均温度变化总体呈逐日上升趋势,日平均气压总体呈逐日下降趋势,日平均相对湿度变化主要表现为污染前期湿度先降低,后期逐渐增大,风速的变化趋势不明显,基本维持在1m/s～1.5m/s之间。
(4)分析表明,重庆主城区边界层气象条件对污染物浓度有最直接的影响。在雾天、晴天和阴天三种典型代表性天气中,从夜间到清晨,出现逆温频率高且空风速较小,但由于夜间污染源排放减弱,污染物浓度呈现自然下降态势；其中雾天由于相对湿度大,水汽的吸附(转化)作用,污染物浓度下降速度比晴天和阴天快。8:00以后由于人群活动及污染排放量的增加,污染物浓度在上述三种代表性天气背景下均呈快速上升态势；从8:00到13:00,阴天升温不及雾天和晴天快且高低空风速也比较小,污染物浓度上升速度也比雾天和晴天快；13:00以后,三种代表性天气背景下重庆主城区边界层逆温均基本或完全消失,大气垂直扩散能力明显增强,污染物浓度开始明显下降；其中,晴天增温快,午后温度高,风速大,PM10浓度下降速度也快；雾天温度增速不及晴天快、且地面风速变化不大,因此PM10浓度降低速度低于晴天；阴天由于午后增温速度明显比晴天和雾天慢,增温幅度也小,风速也小,因而PM10浓度降低速度慢,相对污染浓度高。
(5)通过逆温与昼夜温差相关性分析结果表明,与其他北方城市(如兰州市等)不同,重庆主城区由于冬半年昼夜温差小导致逆温强度弱、厚度薄,从而造成其冬半年轻度污染天气相对较多,很少出现中度及以上污染天气,这一点与北方城市冬季易出现重度污染的状况存在明显的差异。对重庆市大气扩散能力的分析结果也表明,湍流动能越强,污染物的垂直扩散能力越强,污染物浓度越低；但总体而言,重庆主城区污染物在大气中水平方向由平均风速完成的平流输送还是明显强于由湍流完成的垂直方向的输送与扩散。
(6)数值模拟结果显示,WRF-Chem模式对重庆主城区大气边界层气象场有较好的模拟效果,其中MYJ方案对边界层温度场和风场模拟效果优于YSU方案。WRF-Chem模式基本能够模拟出重庆主城区PM10、SO2和N02浓度的日变化特征,但浓度值偏小,对污染物浓度模拟效果同样也是MYJ方案优于YSU方案。详细模拟结果表明,边界层高度(PBLH)能显著影响污染物浓度,通过计算8:00-17：OOPBLH与PM10的相关系数显示,PBLH与1～3小时后的PM10浓度负相关较好,表明白天PBLH明显增大1～3小时后PM10浓度才明显降低。不同天气背景下也略有变化,其中在雾天和阴天,PBLH;斗高后2-3小时PM10浓度才有明显下降,而在晴天PBLH升高后1～2小时PM10浓度就会明显下降,表明大气边界层气象条件变化速度越快,污染物稀释扩散响应时间也越短。上述PM10浓度随PBLH变化而变化的事实,能够较好地解释PM10日变化特征的成因。通过数值模拟还发现,边界层风场对污染物浓度变化也有明显的作用,在夜间,重庆主城区主要受下沉气流影响,对污染物的向上垂直扩散作用非常弱；在白天上午,由于建筑施工、机动车尾气等污染排放量的增加,当受逆温或夜间下沉气流持续控制时污染物的向上垂直扩散作用很弱,近地面污染物浓度会快速增加；相反,当日出后边界层专为上升气流时污染物的向上垂直扩散能力会增强,污染物浓度的增加速度会减慢,尤其在高空风速大且为强上升气流时,强烈的抽吸作用使得大气垂直扩散能力得到显著增强,能有效地抑制污染物浓度的增长态势。通常到午后,太阳辐射强烈,大气边界层内上升气流达到最强,PBLH达到最大,大气边界层垂直扩散能力最强,因而污染物浓度达到一日中的谷值点。在污染天气向非污染天气转换过程中,大气边界层的风速会明显增强且为上升气流,边界层高度会明显增高,湍流动能也明显增强,受大气的水平输送能力和垂直扩散能力都增强的共同作用,重庆主城区大气污染物浓度会明显下降,空气质量转好,由此较为细致地揭示了该区边界层气象条件的变化对空气污染影响的过程与机理。
(7)研究表明,降水对重庆主城区空气污染物也有明显的清除作用,不同强度等级的日降水对PM10、SO2、N02清除效率和对API值降低率均呈现指数变化；连续性降水对污染物清除最有效的是前两天降水,且第二天降水对污染物的清除效率比第一天要高。逐时降水对PM10和S02清除效果好于N02;夜间降水对污染物清除效率比白天降水高;此外降雨对污染浓度上升阶段清除效果好于下降阶段。
基于上述对重庆主城区空气污染时空变化特征及其与气象条件的研究,并利用降水能有效清除污染物的机理,结合该区首要污染物为PM10及其特殊的日变化特点,我们建议可通过局部的试验研究,然后在20::00～次日7:00(夜间)选择火箭、飞机增雨或高楼喷水等作业方式在重庆主城区进行人工干预措施,达到快速清除低空污染物,有效改善空气质量之目的；若选择在上午8:00～12:00可针对PM10浓度出现上升时间段内,通过飞机增雨或高楼喷水等方式进行作业,可能对抑制PM10浓度的升高会起到更好的效果。
As the biggest Chinese city in size and with complex terrain, Chongqing is one of the earliest type of fog-haze air pollution, and the most air-polluted cities in China, which has not only brought harms to health of the citizens, but also influenced the city's investment environment and competitiveness. Based on current situation, this paper firstly, studies the spatial distribution features and annual, monthly and diurnal variation of three main air pollutants (PM10, SO2, and NO2) in the urban areas of Chongqing from2002to2011. Secondly, method of K-means clustering analysis is used to classify the upper air and ground surface weather conditions which affect the diffusion of contaminant. The typical weather types that block the diffusion of pollutants summed up. The paper also analyzes the weather process characteristics of air pollution and the correlation between the local meteorological elements and the pollution concentration.The removal efficiency on atmospheric pollutants of different properties of precipitation is minutely discussed. Moreover, using the WRF-Chem model and observed data analysis, the paper emphatically studies the effects of local boundary layer meteorological features on the pollutants concentration under the conditions of sunny days, cloudy days and fogs in the urban areas of Chongqing. Mechanism of how meteorological conditions lead to serious air pollution is preliminarily uncovered. The main findings of the research are as follow:
(1) The spatial distribution characteristics of air pollution in the main urban region of Chongqing:the highest PM10pollution areas are the six industrial and commercial core areas, and the SO2pollutant is mainly distributed in the industrial area, while the NO2pollutant mainly appears in the most prosperous business areas and busy roads.
(2) The annual variation of the three main pollutants (PM10, SO2and NO2) concentration in the urban areas of Chongqing is declining on the whole, and the air quality is improved year by year. The highest pollution period is late autumn, winter and early spring (from November to March), especially in December and followed by November. The hourly average concentration of PM10presents the characteristics of "double peak and double valley" diurnal variation with a period of12hours. The peak appears at around12:00and22:00in winter half, while the lowest value appears at around7:00and18:00. But, the peak appears at around11:00and23:00in summer half, while the lowest value appears at around7:00and17:00.The concentration of SO2presents the characteristics of "single peak" diurnal variation. The peak appears at about12:00to13:00in winter half and10:00to11:00in summer half. The average of NO2concentration presents the characteristics of insignificant "double peak and double valley" diurnal variation. The main peak appears clear at approximately20:00and weak at about10:00to12:00.
(3) The500hPa weather situations are classified by the method of K-means clustering analysis in Chongqing. The result shows that the high altitude weather situations are mainly the one trough and one ridge, zonal circulation, two troughs and one ridge, west high and east low and trough moving eastward. In the period of air pollution, the overall trend of the daily average surface temperature is increasing, the daily average pressure is decreasing, the daily average relative humidity changes mainly for pre period of pollution is decreasing first, then gradually increasing, and the wind speed remains from1m/s to1.5m/s.
(4) Analysis showed that the main city of Chongqing meteorological boundary conditions have the most direct impact on the concentration of pollutants. From night to morning, because three typical representative weather conditions of foggy, sunlit and cloudy are in the thermal inversion layer and wind speed is small in high altitude and near the surface, the upward spread of PM10is weak. The pollutant concentration is significantly weakened with the declination of pollution emissions. Due to the high relative humidity and the adsorption of water vapor, the rate of pollutants concentration declines faster that in the fog than sunny and cloudy. After8:00, as the increase of pollution emissions by human activity, the pollutant's concentration rises fast. However, during the8:00to13:00, the temperature rises more slowly in cloudy days than in sunny days and fogs, and the wind speed is too small in cloudy days directly leading to that the rate of pollutants concentration increasing is faster in cloudy days than that in sunny days and fogs. After13:00, boundary layer inversion is disappeared under the background of the three weather types. Therefore, the atmospheric diffusion is notably enhanced and pollutants concentrations begin to decline obviously. There are higher temperature and faster warming in sunny days, and the wind speed is large in high latitude and near the surface, so the concentrations of PM10decrease fast. The rate of warming is slower in foggy days than sunny days, so the rate of PM10concentration declining is smaller than in sunny days, which is similar in cloudy days.
(5) Through comparative analysis of inversion and diurnal temperature showed that the reason of weak inversion and thin inversion is small temperature difference between day and night in winter months in main city of Chongqing, unlike other northern cities (such as Lanzhou, etc.). Just the characteristic of weak inversion and thin inversion is caused slightly polluted weather more than moderately polluted weather in Chongqing city. This is obvious differences between in Chongqing city and in northern city. Through analysis of the changes of turbulent kinetic energy and correlation analysis of turbulent kinetic energy and pollutant concentration in different weather that the turbulent kinetic energy is stronger, vertical diffusion of pollutants is stronger, and the concentration of pollutant is lower.Diffusion capacity of pollutants is transported by the mean wind speed in horizontal advection significantly stronger than it is transported by the turbulent in vertical direction.
(6) WRF-Chem model has good simulated effects on atmospheric boundary layer in urban areas of Chongqing. Compared the two boundary layer programs, surface temperature and boundary layer wind field simulated by MYJ is better than that of YSU. The WRF-Chem model can simulate the diurnal variation of PM10, SO2and NO2concentrations in Chongqing main city, but the concentrations were significantly smaller. The concentration of pollutants simulated by MYJ is also better than that of YSU. The results of numerical simulation show that the height of boundary layer has effects significantly on pollutants concentration. As atmospheric boundary layer is stable and PBLH is relatively low at night, the vertical diffusivity of atmosphere is weak. As a result of less emission after nightfall, PM10will decrease gradually, and the pre-dawn reached its lowest point.As the increasing of solar radiation by day, PBLH increases rapidly and the vertical diffusivity space of pollutants enlarges evidently. Correlation coefficient between PBLH and PM10at8:00-17:00is examined. The result shows that PBLH is negatively related with PM101or3hours later. So, for PBLH's effect on PM10, there is a lag effect of1or3hours. The PBLH trends vary in different weather conditions, which results in obvious difference in the effects on pollutants concentration. In foggy and cloudy days, PM10decreases evidently2-3hours after increasing of PBLH. However, PM10decreases evidently1-2hours after increasing of PBLH in sunny days. The reason of PM10concentration change delay1-3hours of PBLH change is that PM10(or other contaminants) is gradually changed under the action of the atmospheric dispersion. It is a slow gradual process. SO the meteorological condition change of atmospheric boundary layer is faster, dispersion response time of pollutant is shorter.Analysis of effects of PBLH variation on PM10can better explain the characteristics of diurnal variation of PM10. Therefore, forecast of PBLH can briefly figure out the hourly variation trends of PM10, which provides technological support for pollutants forecast. The study also finds that variations of boundary layer wind field and pollutants concentration are good indicators. The urban areas of Chongqing are influenced by downdraft at night, which has weak effect on the vertical diffusivity of pollutants. Artificial emission increases in the morning. Downdraft weakens the vertical diffusivity of pollutants, which results in rapidly increasing of pollutants concentration. In the contrast, when there is updraft, the vertical diffusivity of pollutants is strong and the increasing speed of pollutants slows down. It's especially obvious when there is a high upper air wind speed and strong updraft. The strong pumping action strengthens the vertical diffusivity of atmosphere significantly to control the increasing of pollutants. In the afternoon, there is mainly strong updraft in the boundary layer as a result of strong solar radiation. The vertical diffusivity of atmospheric boundary layer is the strongest, which results in an obvious decreasing trend of the pollutants concentration, and reached the day in the valley point of the pollution concentration. When it is from pollution to non-polluted in atmospheric boundary layer, the wind speed will be significantly enhanced and is updraft, the boundary layer height will be significantly increased and the turbulent kinetic energy will be significantly enhanced. The air pollutant concentrations would be obviously reduced when horizontal diffusion and vertical diffusion capabilities are enhanced in the atmosphere.
(7)Precipitation has obvious scavenging effect on atmospheric pollutants. In addition, precipitation at different levels presents the exponential change of the scavenging effect on PM10, SO2, NO2and API reduction rate. The removal effect of continuous precipitation is best in the first two days. And the second day is better than the first day. The removal effect of hourly precipitation on PM10and SO2is better than that on NO2. And the night is better than the daytime. The removal effect of precipitation is better in the rising phase than the declining phase.
Based on the research of temporal variation of air pollution and meteorological conditions, and used of the mechanism of precipitation can effectively remove contaminants,and combined with the special primary pollutant PM10diurnal variation characteristics in the main city of Chongqing, rocket and artificial precipitation and water spray from high-rise from20:00to7:00can be used to improve air quality. And from8:00to12:00, aircraft rainfall or high-rise sprinkler can be better used to block the PM10concentration increasing.
引文
[1]2011年重庆市国民经济和社会发展公报.重庆市人民政府,2012.
[2]王式功,杨德保,李腊平,等.兰州城区冬半年冷锋活动及其对空气污染的影响[J].高原气象,1998,17(2):142-149.
[3]杨德保,尚可政,王式功,等.兰州城市空气污染的天气分型与统计分析[M].城市空气污染预报研究,兰州大学出版社,2002,191-198.
[4]王式功,杨德保,黄建国.兰州市八种主要空气污染物浓度分布类型及其相互关系[J].兰州大学学报(自然科学版),1996,32(1):121-125.
[5]杨德保,王式功,黄建国,等.兰州冬季大气污染与天气形势的统计分析.复杂地形上大气边界层和大气扩散的研究[M].北京,气象出版社,1993,159-165.
[6]杨德保,王式功,黄建国.兰州市区大气污染与气象条件的关系[J].兰州大学学报(自然科学版),1994,30(1)：132-136.
[7]王式功,杨德保,尚可政,等.兰州市城区冬半年低空风特征及其与空气污染的关系[J].兰州大学学报(自然科学版),1997,33(3):97-105.
[8]尚可政,王式功,杨德保,等.兰州冬季空气污染与地面气象要素的关系[J].甘肃科学学报,1999,11：1-5.
[9]孟燕军,程丛兰.影响北京大气污染物变化地面天气形势分析[J].气象,2002,28(4):42-47.
[10]叶堤.重庆市空气污染持续过程特征及其气象成因分析[J].江苏环境科技,2007,20(4):57-60.
[11]叶堤,王飞,陈德蓉.重庆市多年大气混合层厚度变化特征及其对空气质量的影响分析[J].气象与环境学报,2008,24(4):41-44.
[12]唐燕秋,陈佳,熊强,等.重庆市多年空气污染指数分析及大气污染控制对策[J].四川环境,2005,24(6):80-98.
[13]刘永明,陈盛粱,周竹渝,等.重庆市主城区空气污染成因分析及改善大气扩散条件的措施建议[J].重庆环境科学,2001,24(3):P22-25.
[14]盂小峰,徐刚.重庆主城区空气质量时空分布及原因分析[J].亚热带资源与环境学报,2010,5(4):37-42
[15]胡春梅,刘德,陈道劲.重庆市空气污染扩散气象条件指标研究[J].气象科技,2009,37(6):665-669.
[16]蒋昌潭,张卫东.重庆市主城“蓝天行动”：典型山地城市大气污染控制实例[M].北京,中国环境科学出版社,2009.
[17]Nuria Galindo,Montse Varea and Juan Gil-Molto.The Influence of Meteorology on Particulate Matter Concentrations at an Urban Mediterranean Location [J].Water Air Soil Pollut,2011,215:365-372.
[18]G. R. McGregor,and D. Bamzelis.Synoptic Typing and its Application to the Investigation of Weather Air Pollution Relationships, Birmingham,United Kingdom[J]. Theor. Appl. Climatol. 1995,51,223-236.
[19]H. Flocas & A. Kelessis & C. Helmis & M. Petrakakis Synoptic and local scale atmospheric circulation associated with air pollution episodes in an urban Mediterranean area[J]. Theor Appl Climatol,2009,95:265-277.
[20]George Kallos, Pavlos kassomenos and Roger A. Plelke.Synoptic and mesoscale weather conditions during air pollution episodes in Athens, Greece。Boundary-layer meteorology, 1993,62:163-184.
[21]杨清玲,陈刚才,马宁,等.重庆市主城区机动车污染分担率研究[J].西南师范大学学报(自然科学版).2009,34(4):0173-0178.
[22]刘萍,翟崇治,余家燕,等.重庆市道路交通空气监测现状及控制对策[J].四川环境.2012,31(1):37-41.
[23]刘晓刚.重庆市主城区二氧化硫地面浓度场分布特征及污染防治对策研究田].重庆：重庆大学硕士学位论文,2007.
[24]郭军,任国玉.天津地区近40年日照时数变化特征及其影响因素[J].气象科技,2006,34(4):415-420.
[25]XiaoBo Zheng TianLiang Zhao, et al.Trends in sunshine duration and atmospheric visibility in the Yunnan-Guizhou Plateau,1961-2005.Sciences in Cold and Arid Regions,2011, 3(2):179-184.
[26]魏玉香,童尧青,银燕,等.南京SO2、NO2和PM1o变化特征及其与气象条件的关系[J].大气科学学报,2009,32(3):451-457.
[27]焦建丽,康雯英,王军,等.河南省日照时数时空变化分析[J].气象与环境科学,2008,31:4-6.
[28]李琼,李福娇,叶燕翔,等.珠江三角洲地区天气类型与污染潜势及污染浓度的关系[J].热带气象学报,1999,15(4):363-369.
[29]李国翠,连志鸾,郭卫红,等.石家庄市污染日特征及其天气背景分析[J].气象科技,2006,34(6):674-678.
[30]唐燕秋,陈佳,熊强,等.重庆市多年空气污染指数分析及大气污染控制对策[J].四川环境,2005,24(6):80-82.
[31]周慧,王自发,安俊岭,等.城市空气污染持续维持机制研究I-2002年西安市空气污染持续维持过程分析及其气象成因[J].气候与环境研究,2005,10(1):124-131.
[32]Helmut Mayer. Air pollution in cities[J]. Atmos Environ.1999,33:4029-4037,
[33]杨利敏,李良福.充分发挥气象科技优势促进重庆空气质量根本好转[J].气象软科学,2006,4:53-57.
[34]王艳秋,杨晓丽.哈尔滨市降水形势对大气污染物浓度稀释的影响[J].自然灾害学报,2007,16(5):65-68.
[35]李瑞,王旭.乌鲁木齐降水对大气污染的影响[J].沙漠与绿洲气象.2007,1(2):13-15.
[36]向敏.我国重点城市大气污染时空分布特征分析[J].广州化工.201 1,39(21):127-130.
[37]李霞,杨青,吴彦.乌鲁木齐地区雪和雨对气溶胶湿清除能力的比较研究[J].中国沙漠.2003,23(5):560-564.
[38]周甘霖.兰州市空气污染特征及其与气象条件关系研究[D].兰州：兰州大学硕士论文,2012.
[39]杜荣光,齐冰,郭惠惠,等.杭州市大气逆温特征及对空气污染物浓度的影响[J].气象与环境学报,2011,27(4):49-53.
[40]赵琦,张丹,叶堤,等.重庆主城大气PM10的源解析研究[J].三峡环境与生态,2008,1(3):14-17.
[41]夏恒霞.北京城区逆温气象特征及其对大气污染的影响.城市管理与科技[J].2004,6(2):63-68.
[42]姜大膀,王式功,郎咸梅等.兰州市区低空大气温度层结特征及其与空气污染的关系.兰州 大学学报[J].2001,37(4):133-139.
[43]郑庆锋,史军.上海地区大气贴地逆温的气候特征.干旱气象[J].2011,29(2):195-200.
[44]张建辉K-means聚类算法研究及应用[D].武汉：武汉理工大学硕士论文,2007.
[45]康雪,李柏,吴蕾,等.基于K-means聚类分析的风廓线雷达降水数据判别方法[J]气象科技.2013,41(5):818-822.
[46]田宏伟,谈建国,杜子璇.用TSI天气分型方法分析上海环境空气质量[J].气象与环境科学.2008,31(1):51-55.
[47]H. Choi and M. S. Speer. Effects of atmospheric circulation and boundary layer structure on the dispersion of suspended particulates in the Seoul Metropolitan area [J]. Meteorol Atmos Phys.2006,92:239-254.
[48]Peter A. Tanner and po-tak law. Eeffects of synoptic weather systems upon the air quality in an asian megacity [J]. Water air and soil pollution.2002,136:105-124.
[49]S.Akpinar, Hakan F.Oztop and Ebru Kavak Akpinar. Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazig, Turkey [J]. Environ monit assess.2008,146:211-224.
[50]A.J.Manning, K.J.Nicholson, and D.R.middleton, et al. Field study of wind and traffic to test a street canyon pollution model[J].Environmental monitoring and assessment.2000,60: 283-313.
[51]M. R. Soler, J. Hinojosa, and M. Bravo, et al. Analyzing the basic features of different complex terrain flows by means of a doppler sodar and a numerical model:some implications for air pollution problems [J].Meteorol Atmos Phys.2004,85:141-154.
[52]Alexander Baklanov. Application of CFD methods for modelling in air pollution problems: possibilities and gaps [J]. Environmental monitoring and assessment.2000,65:181-189.
[53]D. S. Chen, S. Y. Cheng, and J. B. Li, et al. Application of lidar technique and MM5-CMAQ modeling approach for the assessment of winter PM10 air pollution:a case study in Beijing, China Water air soil pollution.2007,181:409-427.
[54]Yassine Charabi, Ali Al-bulooshi and Sultan Al-yahyai. Assessment of the impact of the meteorological meso-scale circulation on air quality in arid subtropical region [J]. Environ monit assess.2013,185:2329-2342.
[55]Nikolai Nawri and Knut Harstveit. Variability of surface wind directions over finnmark, norway, and coupling to the larger-scale atmospheric circulation [J].Theor Appl Climatol.2012,107:15-33.
[56]Zi-huali, Junyang, and Chun-eshi, et al. Urbanization effects on fog in china:field research and modeling [J]. Pure Appl. Geophys.2012,169:927-939.
[57]H.J.S. Fernando, S.M. Lee, and J. Anderson, et al. Urban fluid mechanics:air circulation and contaminant dispersion in cities [J]. Environmental fluid mechanics.2001,1:107-164.
[58]P. Goswami and J. Baruah. Urban air pollution:process identification, impact analysis and evaluation of forecast potential [J]. Meteorol Atmos Phys.2011,110:103-122.
[59]P. P. Kastendeuch and G. Najjar. Upper-air wind profiles investigation for tropospheric circulation study [J]. Theor. Appl. Climatol.2003,75:149-165.
[60]L. Makra, J. Mika, and A. Bartzokas, et al. An objective classification system of air mass types for szeged, hungary, with special interest in air pollution levels [J]. Meteorol Atmos Phys.2006,92:115-137.
[61]A Gohm, F. Harnisch and J. Vergeiner, et al. Air pollution transport in an alpine valley:results from airborne and ground-based observations [J]. Boundary-layer meteorology. 2009,131:441-463.
[62]Jin Young Kim, Sang-woo Kim, and Young Sung Ghim, et al. Aerosol properties at Gosan in Korea during two pollution episodes caused by contrasting weather conditions [J]. Asia-pacific J. Atmos. Sci..2012,48(1):25-33.
[63]Martin Ekniston. A numerical study of atmospheric pollution over complex terrain in Swtzerland [J]. Boundary-layer meteorology.1987,41-75.
[64]R. A. Almbauer, M. Piringer, and K. Baumann, et al. Analysis of the daily variations of wintertime air pollution concentrations in the city of Graz, Austria [J]. Environmental monitoring and assessment.2000,65:79-87.
[65]Nobumitsu Tsunematsu, Kenji Kai, and Takuya Matsumoto. The influence of synoptic-scale air flow and local circulation on the dust layer height in the north of the taklimakan desert [J]. Water air and soil pollution.2005,5:175-193.
[66]Nuria Galindo, Montse Varea and Juan Gil-molto. The influence of meteorology on particulate matter concentrations at an urban mediterranean location [J]. Water air soil pollution.2011,215:365-372.
[67]Gu"nther Za"ngl. The impact of weak synoptic forcing on the valley-wind circulation in the alpine inn valley [J]. Meteorol atmos phys.2009,105:37-53.
[68]M. Colacino and L. Dell'osso. The local atmospheric circulation in the rome area:surface observations [J]. Boundary-layer meteorology.1978,14:133-151.
[69]G. R. Mcgregor and D. Bamzelis. Synoptic typing and its application to the investigation of weather air pollution relationships, Birmingham, United kingdom [J]. Theor. Appl. Climatol.1995,51:223-236.
[70]H. Flocas, A. Kelessis and C. Helmis. Synoptic and local scale atmospheric circulation associated with air pollution episodes in an urban Mediterranean area [J]. Theor. Appl. Climatol.2009,95:265-277.
[71]George Kallos, Pavlos Kassomenos and Roger A. Plelke. Synoptic and mesoscale weather conditions during air pollution episodes in Athens, Greece [J]. Boundary-layer meteorology. 1993,62:163-184.
[72]Carlo Montes, Ricardo C. Munoz and Jorge F. Perez-quezada. Surface atmospheric circulation patterns and associated minimum temperatures in the Maipo and Casablanca valleys,central Chile [J]. Theor. Appl. Climatol.2013,111:275-284.
[73]Alessio Pollice and Giovanna Jona Lasinio. Spatiotemporal analysis of the PM10 concentration over the Taranto area [J]. Environ Monit Assess.2010,162:177-190.
[74]Cristina Mangia, Emilio A. L. Gianicolo and Antonella Rruni, et al. Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessment [J]. Environ Monit Assess.2013,185:1719-1735.
[75]Hamdy K. Elminir. Relative influence of air pollutants and weather conditions on solar radiation-part 1:relationship of air pollutants with weather conditions [J]. Meteorol Atmos Phys.2007,96:245-256.
[76]Surachai Sathitkunarat, Prungchan Wongwises, and Rudklao Pan-aram, et al. Numerical simulation of terrain-induced mesoscale circulation in the Chiang Mai area, Thailand [J]. Meteorol Atmos Phys.2008,102:113-121.
[77]Nadir Ilten and A. Tiilay Selici. Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey [J]. Environ Monit Assess.2008,140:267-277.
[78]王颖.复杂下垫面下空气污染数值模拟研究.兰州：兰州大学博士论文,2010.
[79]Xuemei Wang, ZhiyongWu and Guixiong Liang. WRF/CHEM modeling of impacts of weather conditions modified by urban expansion on secondary organic aerosol formation over Pearl River Delta [J]. Particuology.2009,7:384-391.
[80]Xueyuan Wang, Xin-Zhong Liang, Weimei Jiang, et al.WRF-Chem simulation of East Asian air quality:Sensitivity to temporal and vertical emissions distributions [J].Atmospheric Environment.2010,44:660-669.
[81]Renate Forkel, Johannes Werhahn and Ayoe Buus Hansen, et al. Effect of aerosol-radiation feedback on regional air quality—A case study with WRF/Chem [J].Atmospheric Environment.2012,53:202-211.
[82]Pablo E. Saide, Gregory R. Carmichael, Scott N. Spak,et al. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnalconditions and complex terrain using WRF-Chem CO tracer model[J].Atmospheric Environment.2011,45:2769-2780.
[83]马欣,陈东升,高庆先,等.应用WRF-Chem模式模拟京津冀地区气溶胶污染对夏季气象条件的影响[J].资源科学.2012,34(8)：1408-1415.
[84]韩素芹,冯银厂,边海,等.天津大气污染物日变化特征WRF-Chem数值模拟[J].中国环境科学.2008,28(9):828-832.
[85]Roland B.Stull.边界层气象学导论[M].杨长新译.北京,气象出版社,1991.
[86]胡隐樵,张强.兰州山谷大气污染的物理机制与防治对策[J].中国环境科学,1999,19(2):119-122.
[87]Zhang L, C H Chen, and J Murlis. Study on Winter Air Pollution Control in Lanzhou, China [J]. Water Air & Soil Pollution.2001,127:351-372.
[88]张强,吕世华,张光庶.山谷城市大气边界层结构及输送能力[J],高原气象,2003,22(4):346-353.
[89]姜金华,彭新东.复杂地形城市冬季大气污染的数值模拟研究[J].高原气象,2002,21：1-7.
[90]安兴琴,安俊岭,吕世华,等.复杂地形城市S02扩散特征的模拟研究[J],城市环境与城市生态,2005,18(3):23-26.
[91]缪国军,张镭,舒红.利用WRF对兰州冬季大气边界层的数值模拟[J].气象科学,2007,27(2):169-175.
[92]王海龙,张镭,陈长和,等.兰州市东部地区冬季低空风场和温度场分析[J].兰州大学学报(自然科学版),1999,35(14):17-123.
[93]洪钟祥,胡非.大气污染预测的理论和方法研究进展[J].气候与环境研究,1999,4(3):225-30.
[94]王自发,庞成明,朱江,等.大气环境数值模拟研究新进展[J].大气科学,2008,32(4):987-995.
[95]Kindap T. Identifying the Trans-Boundary Transport of Air Pollutants to the City of Istanbul Under Specific Weather Conditions [J]. Water Air Soil Pollut,2008,18(9):279-289.
[96]Arasa R., M.R. Soler, S. Ortega, et al. A performance evaluation of MM5/MNEQA/CMAQ air quality modelling system to forecast ozone concentrations in Catalonia. Journal of Mediterranean Meteorology & Climatology,2010,7:11-23
[97]许莹.兰州市空气污染现状统计分析[J].环境研究与监测,2004,4:29-32.
[98]张美根,韩志伟,雷孝恩.城市空气污染预报方法简述[J].气候与环境研究,2001,6(1)：113-118.
[99]王勤耕,夏思佳,万袆雪,等.当前城市空气质量预报方法存在的问题及新思路[J].环境科学技术,2009,32(4)：189-192.
[100]Seigneur C. Current status of air quality models for particulate matter [J]. Journal of Air and Waste Management Association,2001,51:1508-1521.
[101]佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测,2006,22(2):69-71.
[102]LeDuc S., K. Schere, J. Godowitch, et al. Models3/CMAQ Applications which illustrate capability and functionality. Air Pollution Modeling and Its Application,2004, Part 7, 737-738.
[103]房小怡,蒋维楣,吴涧,等.城市空气质量数值预报模式系统及其应用[J].环境科学学报,2004,24(1)：111-115.
[104]谢学军,李杰,王自发.2010.兰州城区冬季大气污染物日变化的数值模拟[J].气候与环境研究,15(5)：695-703.
[105]Smyth S. C., W. M. Jiang, D. Z. Yin, et al. Evaluation of CMAQ O3 and PM2.5 Performance using Pacific 2001 measurement data [J]. Atmospheric Environment,2006,40: 2735-2749.
[106]Phillipsa S. B., P. L. Finkelstein. Comparison of spatial patterns of pollutant distribution with CMAQ predictions [J]. Atmospheric Environment,2006,40:4999-5009.
[107]Isakov V, J. S. Irwin, J. Ching. Using CMAQ for Exposure Modeling and Characterizing the Subgrid Variability for Exposure Estimates [J]. Journal of Applied Meteorology and Climatology,2007,46:1354-1371.
[108]Jiang W., S. Smyth. E. Giroux. Differences between CMAQ fine mode particle and PM2.5 concentrations and their impact on model performance evaluation in the lower Fraser valley [J]. Atmospheric Environment,2006,40:4973-4985.
[109]Sokhi R. S., R. San Jose, N. Kitwiroon, et al. Prediction of ozone levels in London using the MM5-CMAQ modelling system [J]. Environmental Modelling & Software,2006,21: 566-576.
[110]Eder B., D. Kang, R. Mathur, et al. An operational evaluation of the Eta-CMAQ air quality forecast model [J]. Atmospheric Environment,2006,40:4894-4905.
[111]Park S. K., A. Marmur, S. B. Kim, et al. Evaluation of fine particle number concentrations in CMAQ [J]. Aerosol Science and Technology,2006,40:985-996.
[112]Zhanga K. M., A. S. Wexlerb. Modeling urban and regional aerosols—Development of the UCD Aerosol Module and implementation in CMAQ model [J]. Atmospheric Environment 2008,42:3166-3178.
[113]Park S. K., C. E. Cobb, K. Wade, et al. Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations [J]. Atmospheric Environment,2006,40:563-573.
[114]Gilliland A. B., C. Hogrefe, R. W. Pinder, et al. Dynamic evaluation of regional air quality models:Assessing changes in 03 stemming from changes in emissions and meteorology [J]. Atmospheric Environment,2008,42:5110-5123.
[115]Chuang M. T., J. S. Fu, C. J. Jang, et al. Simulation of long-range transport aerosols from the Asian Continent to Taiwan by a Southward Asian high-pressure system [J]. Science of the total environment,2008,406:168-179.
[116]Kubilay N., S. Nickovic, C. Moulin, et al. An illustration of the transport and deposition of mineral dust onto the eastern Mediterranean [J]. Atmospheric Environment,2000,34: 1293-1303.
[117]Rodriguez S., X. Querol, A. Alastuey, et al. Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain [J]. Atmospheric Environment,2001,35(14): 2433-2447.
[118]Lelieveld J., H. Berresheim, S. Borrmann, et al. Global air pollution crossroads over the Mediterranean [J]. Science,2002,298:794-799.
[119]Koo Y-S., S-T. Kim, H-Y Yun, et al.The simulation of aerosol transport over East Asia region [J]. Atmospheric Research,2008,90:264-271.
[120]Kindap T.. Identifying the Trans-Boundary Transport of Air Pollutants to the City of Istanbul Under Specific Weather Conditions [J]. Water Air Soil Pollute,2008,189:279-289.
[121]陈小敏,李轲.重庆主城区人工增雨对空气质量的影响分析[J].西南大学学报,2010,35(6):152-156.
[122]Jimenez-Guerrer P., O. Jorba, J. M. Baldasan, et al. The use of a modelling system as a tool for air quality management:Annual high-resolution simulations and evaluation [J]. Science of the total environment,2008,390:323-340.
[123]安兴琴,左洪超,吕世华,等.Models3空气质量模式对兰州市污染物输送的模拟[J].高原气象,2005,24(5):748-756.
[124]Sistla G., N. Zhou, W. Hao, et al. Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies [J]. Atmospheric Environment, 2001,30:2011-2025.
[125]Vaughan J., B. Lamb, C. Frei, et al. A numerical daily air quality forecast system for The Pacific Northwest [J]. Bulletin of the American Meteorological Society,2004,85:549-561.
[126]Bossioli E., M. Tombrou, A. Dandou, et al. The Role of Planetary Boundary-Layer Parameterizations in the Air Quality of an Urban Area with Complex Topography Boundary-Layer [J]. Meteorol,2009,131:53-72.
[2]王式功,杨德保,李腊平,等.兰州城区冬半年冷锋活动及其对空气污染的影响[J].高原气象,1998,17(2):142-149.
[3]杨德保,尚可政,王式功,等.兰州城市空气污染的天气分型与统计分析[M].城市空气污染预报研究,兰州大学出版社,2002,191-198.
[4]王式功,杨德保,黄建国.兰州市八种主要空气污染物浓度分布类型及其相互关系[J].兰州大学学报(自然科学版),1996,32(1):121-125.
[5]杨德保,王式功,黄建国,等.兰州冬季大气污染与天气形势的统计分析.复杂地形上大气边界层和大气扩散的研究[M].北京,气象出版社,1993,159-165.
[6]杨德保,王式功,黄建国.兰州市区大气污染与气象条件的关系[J].兰州大学学报(自然科学版),1994,30(1)：132-136.
[7]王式功,杨德保,尚可政,等.兰州市城区冬半年低空风特征及其与空气污染的关系[J].兰州大学学报(自然科学版),1997,33(3):97-105.
[8]尚可政,王式功,杨德保,等.兰州冬季空气污染与地面气象要素的关系[J].甘肃科学学报,1999,11：1-5.
[9]孟燕军,程丛兰.影响北京大气污染物变化地面天气形势分析[J].气象,2002,28(4):42-47.
[10]叶堤.重庆市空气污染持续过程特征及其气象成因分析[J].江苏环境科技,2007,20(4):57-60.
[11]叶堤,王飞,陈德蓉.重庆市多年大气混合层厚度变化特征及其对空气质量的影响分析[J].气象与环境学报,2008,24(4):41-44.
[12]唐燕秋,陈佳,熊强,等.重庆市多年空气污染指数分析及大气污染控制对策[J].四川环境,2005,24(6):80-98.
[13]刘永明,陈盛粱,周竹渝,等.重庆市主城区空气污染成因分析及改善大气扩散条件的措施建议[J].重庆环境科学,2001,24(3):P22-25.
[14]盂小峰,徐刚.重庆主城区空气质量时空分布及原因分析[J].亚热带资源与环境学报,2010,5(4):37-42
[15]胡春梅,刘德,陈道劲.重庆市空气污染扩散气象条件指标研究[J].气象科技,2009,37(6):665-669.
[16]蒋昌潭,张卫东.重庆市主城“蓝天行动”：典型山地城市大气污染控制实例[M].北京,中国环境科学出版社,2009.
[17]Nuria Galindo,Montse Varea and Juan Gil-Molto.The Influence of Meteorology on Particulate Matter Concentrations at an Urban Mediterranean Location [J].Water Air Soil Pollut,2011,215:365-372.
[18]G. R. McGregor,and D. Bamzelis.Synoptic Typing and its Application to the Investigation of Weather Air Pollution Relationships, Birmingham,United Kingdom[J]. Theor. Appl. Climatol. 1995,51,223-236.
[19]H. Flocas & A. Kelessis & C. Helmis & M. Petrakakis Synoptic and local scale atmospheric circulation associated with air pollution episodes in an urban Mediterranean area[J]. Theor Appl Climatol,2009,95:265-277.
[20]George Kallos, Pavlos kassomenos and Roger A. Plelke.Synoptic and mesoscale weather conditions during air pollution episodes in Athens, Greece。Boundary-layer meteorology, 1993,62:163-184.
[21]杨清玲,陈刚才,马宁,等.重庆市主城区机动车污染分担率研究[J].西南师范大学学报(自然科学版).2009,34(4):0173-0178.
[22]刘萍,翟崇治,余家燕,等.重庆市道路交通空气监测现状及控制对策[J].四川环境.2012,31(1):37-41.
[23]刘晓刚.重庆市主城区二氧化硫地面浓度场分布特征及污染防治对策研究田].重庆：重庆大学硕士学位论文,2007.
[24]郭军,任国玉.天津地区近40年日照时数变化特征及其影响因素[J].气象科技,2006,34(4):415-420.
[25]XiaoBo Zheng TianLiang Zhao, et al.Trends in sunshine duration and atmospheric visibility in the Yunnan-Guizhou Plateau,1961-2005.Sciences in Cold and Arid Regions,2011, 3(2):179-184.
[26]魏玉香,童尧青,银燕,等.南京SO2、NO2和PM1o变化特征及其与气象条件的关系[J].大气科学学报,2009,32(3):451-457.
[27]焦建丽,康雯英,王军,等.河南省日照时数时空变化分析[J].气象与环境科学,2008,31:4-6.
[28]李琼,李福娇,叶燕翔,等.珠江三角洲地区天气类型与污染潜势及污染浓度的关系[J].热带气象学报,1999,15(4):363-369.
[29]李国翠,连志鸾,郭卫红,等.石家庄市污染日特征及其天气背景分析[J].气象科技,2006,34(6):674-678.
[30]唐燕秋,陈佳,熊强,等.重庆市多年空气污染指数分析及大气污染控制对策[J].四川环境,2005,24(6):80-82.
[31]周慧,王自发,安俊岭,等.城市空气污染持续维持机制研究I-2002年西安市空气污染持续维持过程分析及其气象成因[J].气候与环境研究,2005,10(1):124-131.
[32]Helmut Mayer. Air pollution in cities[J]. Atmos Environ.1999,33:4029-4037,
[33]杨利敏,李良福.充分发挥气象科技优势促进重庆空气质量根本好转[J].气象软科学,2006,4:53-57.
[34]王艳秋,杨晓丽.哈尔滨市降水形势对大气污染物浓度稀释的影响[J].自然灾害学报,2007,16(5):65-68.
[35]李瑞,王旭.乌鲁木齐降水对大气污染的影响[J].沙漠与绿洲气象.2007,1(2):13-15.
[36]向敏.我国重点城市大气污染时空分布特征分析[J].广州化工.201 1,39(21):127-130.
[37]李霞,杨青,吴彦.乌鲁木齐地区雪和雨对气溶胶湿清除能力的比较研究[J].中国沙漠.2003,23(5):560-564.
[38]周甘霖.兰州市空气污染特征及其与气象条件关系研究[D].兰州：兰州大学硕士论文,2012.
[39]杜荣光,齐冰,郭惠惠,等.杭州市大气逆温特征及对空气污染物浓度的影响[J].气象与环境学报,2011,27(4):49-53.
[40]赵琦,张丹,叶堤,等.重庆主城大气PM10的源解析研究[J].三峡环境与生态,2008,1(3):14-17.
[41]夏恒霞.北京城区逆温气象特征及其对大气污染的影响.城市管理与科技[J].2004,6(2):63-68.
[42]姜大膀,王式功,郎咸梅等.兰州市区低空大气温度层结特征及其与空气污染的关系.兰州 大学学报[J].2001,37(4):133-139.
[43]郑庆锋,史军.上海地区大气贴地逆温的气候特征.干旱气象[J].2011,29(2):195-200.
[44]张建辉K-means聚类算法研究及应用[D].武汉：武汉理工大学硕士论文,2007.
[45]康雪,李柏,吴蕾,等.基于K-means聚类分析的风廓线雷达降水数据判别方法[J]气象科技.2013,41(5):818-822.
[46]田宏伟,谈建国,杜子璇.用TSI天气分型方法分析上海环境空气质量[J].气象与环境科学.2008,31(1):51-55.
[47]H. Choi and M. S. Speer. Effects of atmospheric circulation and boundary layer structure on the dispersion of suspended particulates in the Seoul Metropolitan area [J]. Meteorol Atmos Phys.2006,92:239-254.
[48]Peter A. Tanner and po-tak law. Eeffects of synoptic weather systems upon the air quality in an asian megacity [J]. Water air and soil pollution.2002,136:105-124.
[49]S.Akpinar, Hakan F.Oztop and Ebru Kavak Akpinar. Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazig, Turkey [J]. Environ monit assess.2008,146:211-224.
[50]A.J.Manning, K.J.Nicholson, and D.R.middleton, et al. Field study of wind and traffic to test a street canyon pollution model[J].Environmental monitoring and assessment.2000,60: 283-313.
[51]M. R. Soler, J. Hinojosa, and M. Bravo, et al. Analyzing the basic features of different complex terrain flows by means of a doppler sodar and a numerical model:some implications for air pollution problems [J].Meteorol Atmos Phys.2004,85:141-154.
[52]Alexander Baklanov. Application of CFD methods for modelling in air pollution problems: possibilities and gaps [J]. Environmental monitoring and assessment.2000,65:181-189.
[53]D. S. Chen, S. Y. Cheng, and J. B. Li, et al. Application of lidar technique and MM5-CMAQ modeling approach for the assessment of winter PM10 air pollution:a case study in Beijing, China Water air soil pollution.2007,181:409-427.
[54]Yassine Charabi, Ali Al-bulooshi and Sultan Al-yahyai. Assessment of the impact of the meteorological meso-scale circulation on air quality in arid subtropical region [J]. Environ monit assess.2013,185:2329-2342.
[55]Nikolai Nawri and Knut Harstveit. Variability of surface wind directions over finnmark, norway, and coupling to the larger-scale atmospheric circulation [J].Theor Appl Climatol.2012,107:15-33.
[56]Zi-huali, Junyang, and Chun-eshi, et al. Urbanization effects on fog in china:field research and modeling [J]. Pure Appl. Geophys.2012,169:927-939.
[57]H.J.S. Fernando, S.M. Lee, and J. Anderson, et al. Urban fluid mechanics:air circulation and contaminant dispersion in cities [J]. Environmental fluid mechanics.2001,1:107-164.
[58]P. Goswami and J. Baruah. Urban air pollution:process identification, impact analysis and evaluation of forecast potential [J]. Meteorol Atmos Phys.2011,110:103-122.
[59]P. P. Kastendeuch and G. Najjar. Upper-air wind profiles investigation for tropospheric circulation study [J]. Theor. Appl. Climatol.2003,75:149-165.
[60]L. Makra, J. Mika, and A. Bartzokas, et al. An objective classification system of air mass types for szeged, hungary, with special interest in air pollution levels [J]. Meteorol Atmos Phys.2006,92:115-137.
[61]A Gohm, F. Harnisch and J. Vergeiner, et al. Air pollution transport in an alpine valley:results from airborne and ground-based observations [J]. Boundary-layer meteorology. 2009,131:441-463.
[62]Jin Young Kim, Sang-woo Kim, and Young Sung Ghim, et al. Aerosol properties at Gosan in Korea during two pollution episodes caused by contrasting weather conditions [J]. Asia-pacific J. Atmos. Sci..2012,48(1):25-33.
[63]Martin Ekniston. A numerical study of atmospheric pollution over complex terrain in Swtzerland [J]. Boundary-layer meteorology.1987,41-75.
[64]R. A. Almbauer, M. Piringer, and K. Baumann, et al. Analysis of the daily variations of wintertime air pollution concentrations in the city of Graz, Austria [J]. Environmental monitoring and assessment.2000,65:79-87.
[65]Nobumitsu Tsunematsu, Kenji Kai, and Takuya Matsumoto. The influence of synoptic-scale air flow and local circulation on the dust layer height in the north of the taklimakan desert [J]. Water air and soil pollution.2005,5:175-193.
[66]Nuria Galindo, Montse Varea and Juan Gil-molto. The influence of meteorology on particulate matter concentrations at an urban mediterranean location [J]. Water air soil pollution.2011,215:365-372.
[67]Gu"nther Za"ngl. The impact of weak synoptic forcing on the valley-wind circulation in the alpine inn valley [J]. Meteorol atmos phys.2009,105:37-53.
[68]M. Colacino and L. Dell'osso. The local atmospheric circulation in the rome area:surface observations [J]. Boundary-layer meteorology.1978,14:133-151.
[69]G. R. Mcgregor and D. Bamzelis. Synoptic typing and its application to the investigation of weather air pollution relationships, Birmingham, United kingdom [J]. Theor. Appl. Climatol.1995,51:223-236.
[70]H. Flocas, A. Kelessis and C. Helmis. Synoptic and local scale atmospheric circulation associated with air pollution episodes in an urban Mediterranean area [J]. Theor. Appl. Climatol.2009,95:265-277.
[71]George Kallos, Pavlos Kassomenos and Roger A. Plelke. Synoptic and mesoscale weather conditions during air pollution episodes in Athens, Greece [J]. Boundary-layer meteorology. 1993,62:163-184.
[72]Carlo Montes, Ricardo C. Munoz and Jorge F. Perez-quezada. Surface atmospheric circulation patterns and associated minimum temperatures in the Maipo and Casablanca valleys,central Chile [J]. Theor. Appl. Climatol.2013,111:275-284.
[73]Alessio Pollice and Giovanna Jona Lasinio. Spatiotemporal analysis of the PM10 concentration over the Taranto area [J]. Environ Monit Assess.2010,162:177-190.
[74]Cristina Mangia, Emilio A. L. Gianicolo and Antonella Rruni, et al. Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessment [J]. Environ Monit Assess.2013,185:1719-1735.
[75]Hamdy K. Elminir. Relative influence of air pollutants and weather conditions on solar radiation-part 1:relationship of air pollutants with weather conditions [J]. Meteorol Atmos Phys.2007,96:245-256.
[76]Surachai Sathitkunarat, Prungchan Wongwises, and Rudklao Pan-aram, et al. Numerical simulation of terrain-induced mesoscale circulation in the Chiang Mai area, Thailand [J]. Meteorol Atmos Phys.2008,102:113-121.
[77]Nadir Ilten and A. Tiilay Selici. Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey [J]. Environ Monit Assess.2008,140:267-277.
[78]王颖.复杂下垫面下空气污染数值模拟研究.兰州：兰州大学博士论文,2010.
[79]Xuemei Wang, ZhiyongWu and Guixiong Liang. WRF/CHEM modeling of impacts of weather conditions modified by urban expansion on secondary organic aerosol formation over Pearl River Delta [J]. Particuology.2009,7:384-391.
[80]Xueyuan Wang, Xin-Zhong Liang, Weimei Jiang, et al.WRF-Chem simulation of East Asian air quality:Sensitivity to temporal and vertical emissions distributions [J].Atmospheric Environment.2010,44:660-669.
[81]Renate Forkel, Johannes Werhahn and Ayoe Buus Hansen, et al. Effect of aerosol-radiation feedback on regional air quality—A case study with WRF/Chem [J].Atmospheric Environment.2012,53:202-211.
[82]Pablo E. Saide, Gregory R. Carmichael, Scott N. Spak,et al. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnalconditions and complex terrain using WRF-Chem CO tracer model[J].Atmospheric Environment.2011,45:2769-2780.
[83]马欣,陈东升,高庆先,等.应用WRF-Chem模式模拟京津冀地区气溶胶污染对夏季气象条件的影响[J].资源科学.2012,34(8)：1408-1415.
[84]韩素芹,冯银厂,边海,等.天津大气污染物日变化特征WRF-Chem数值模拟[J].中国环境科学.2008,28(9):828-832.
[85]Roland B.Stull.边界层气象学导论[M].杨长新译.北京,气象出版社,1991.
[86]胡隐樵,张强.兰州山谷大气污染的物理机制与防治对策[J].中国环境科学,1999,19(2):119-122.
[87]Zhang L, C H Chen, and J Murlis. Study on Winter Air Pollution Control in Lanzhou, China [J]. Water Air & Soil Pollution.2001,127:351-372.
[88]张强,吕世华,张光庶.山谷城市大气边界层结构及输送能力[J],高原气象,2003,22(4):346-353.
[89]姜金华,彭新东.复杂地形城市冬季大气污染的数值模拟研究[J].高原气象,2002,21：1-7.
[90]安兴琴,安俊岭,吕世华,等.复杂地形城市S02扩散特征的模拟研究[J],城市环境与城市生态,2005,18(3):23-26.
[91]缪国军,张镭,舒红.利用WRF对兰州冬季大气边界层的数值模拟[J].气象科学,2007,27(2):169-175.
[92]王海龙,张镭,陈长和,等.兰州市东部地区冬季低空风场和温度场分析[J].兰州大学学报(自然科学版),1999,35(14):17-123.
[93]洪钟祥,胡非.大气污染预测的理论和方法研究进展[J].气候与环境研究,1999,4(3):225-30.
[94]王自发,庞成明,朱江,等.大气环境数值模拟研究新进展[J].大气科学,2008,32(4):987-995.
[95]Kindap T. Identifying the Trans-Boundary Transport of Air Pollutants to the City of Istanbul Under Specific Weather Conditions [J]. Water Air Soil Pollut,2008,18(9):279-289.
[96]Arasa R., M.R. Soler, S. Ortega, et al. A performance evaluation of MM5/MNEQA/CMAQ air quality modelling system to forecast ozone concentrations in Catalonia. Journal of Mediterranean Meteorology & Climatology,2010,7:11-23
[97]许莹.兰州市空气污染现状统计分析[J].环境研究与监测,2004,4:29-32.
[98]张美根,韩志伟,雷孝恩.城市空气污染预报方法简述[J].气候与环境研究,2001,6(1)：113-118.
[99]王勤耕,夏思佳,万袆雪,等.当前城市空气质量预报方法存在的问题及新思路[J].环境科学技术,2009,32(4)：189-192.
[100]Seigneur C. Current status of air quality models for particulate matter [J]. Journal of Air and Waste Management Association,2001,51:1508-1521.
[101]佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测,2006,22(2):69-71.
[102]LeDuc S., K. Schere, J. Godowitch, et al. Models3/CMAQ Applications which illustrate capability and functionality. Air Pollution Modeling and Its Application,2004, Part 7, 737-738.
[103]房小怡,蒋维楣,吴涧,等.城市空气质量数值预报模式系统及其应用[J].环境科学学报,2004,24(1)：111-115.
[104]谢学军,李杰,王自发.2010.兰州城区冬季大气污染物日变化的数值模拟[J].气候与环境研究,15(5)：695-703.
[105]Smyth S. C., W. M. Jiang, D. Z. Yin, et al. Evaluation of CMAQ O3 and PM2.5 Performance using Pacific 2001 measurement data [J]. Atmospheric Environment,2006,40: 2735-2749.
[106]Phillipsa S. B., P. L. Finkelstein. Comparison of spatial patterns of pollutant distribution with CMAQ predictions [J]. Atmospheric Environment,2006,40:4999-5009.
[107]Isakov V, J. S. Irwin, J. Ching. Using CMAQ for Exposure Modeling and Characterizing the Subgrid Variability for Exposure Estimates [J]. Journal of Applied Meteorology and Climatology,2007,46:1354-1371.
[108]Jiang W., S. Smyth. E. Giroux. Differences between CMAQ fine mode particle and PM2.5 concentrations and their impact on model performance evaluation in the lower Fraser valley [J]. Atmospheric Environment,2006,40:4973-4985.
[109]Sokhi R. S., R. San Jose, N. Kitwiroon, et al. Prediction of ozone levels in London using the MM5-CMAQ modelling system [J]. Environmental Modelling & Software,2006,21: 566-576.
[110]Eder B., D. Kang, R. Mathur, et al. An operational evaluation of the Eta-CMAQ air quality forecast model [J]. Atmospheric Environment,2006,40:4894-4905.
[111]Park S. K., A. Marmur, S. B. Kim, et al. Evaluation of fine particle number concentrations in CMAQ [J]. Aerosol Science and Technology,2006,40:985-996.
[112]Zhanga K. M., A. S. Wexlerb. Modeling urban and regional aerosols—Development of the UCD Aerosol Module and implementation in CMAQ model [J]. Atmospheric Environment 2008,42:3166-3178.
[113]Park S. K., C. E. Cobb, K. Wade, et al. Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations [J]. Atmospheric Environment,2006,40:563-573.
[114]Gilliland A. B., C. Hogrefe, R. W. Pinder, et al. Dynamic evaluation of regional air quality models:Assessing changes in 03 stemming from changes in emissions and meteorology [J]. Atmospheric Environment,2008,42:5110-5123.
[115]Chuang M. T., J. S. Fu, C. J. Jang, et al. Simulation of long-range transport aerosols from the Asian Continent to Taiwan by a Southward Asian high-pressure system [J]. Science of the total environment,2008,406:168-179.
[116]Kubilay N., S. Nickovic, C. Moulin, et al. An illustration of the transport and deposition of mineral dust onto the eastern Mediterranean [J]. Atmospheric Environment,2000,34: 1293-1303.
[117]Rodriguez S., X. Querol, A. Alastuey, et al. Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain [J]. Atmospheric Environment,2001,35(14): 2433-2447.
[118]Lelieveld J., H. Berresheim, S. Borrmann, et al. Global air pollution crossroads over the Mediterranean [J]. Science,2002,298:794-799.
[119]Koo Y-S., S-T. Kim, H-Y Yun, et al.The simulation of aerosol transport over East Asia region [J]. Atmospheric Research,2008,90:264-271.
[120]Kindap T.. Identifying the Trans-Boundary Transport of Air Pollutants to the City of Istanbul Under Specific Weather Conditions [J]. Water Air Soil Pollute,2008,189:279-289.
[121]陈小敏,李轲.重庆主城区人工增雨对空气质量的影响分析[J].西南大学学报,2010,35(6):152-156.
[122]Jimenez-Guerrer P., O. Jorba, J. M. Baldasan, et al. The use of a modelling system as a tool for air quality management:Annual high-resolution simulations and evaluation [J]. Science of the total environment,2008,390:323-340.
[123]安兴琴,左洪超,吕世华,等.Models3空气质量模式对兰州市污染物输送的模拟[J].高原气象,2005,24(5):748-756.
[124]Sistla G., N. Zhou, W. Hao, et al. Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies [J]. Atmospheric Environment, 2001,30:2011-2025.
[125]Vaughan J., B. Lamb, C. Frei, et al. A numerical daily air quality forecast system for The Pacific Northwest [J]. Bulletin of the American Meteorological Society,2004,85:549-561.
[126]Bossioli E., M. Tombrou, A. Dandou, et al. The Role of Planetary Boundary-Layer Parameterizations in the Air Quality of an Urban Area with Complex Topography Boundary-Layer [J]. Meteorol,2009,131:53-72.
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